Deep learning for lung X-ray image recognition has emerged as a prominent research area.The challenge lies in the small,complexly shaped lesion areas within lung X-rays,where the boundary be-tween the lesion and normal tissue is often unclear,complicating feature extraction in pneumonia images.This paper introduces a Dual Res-Transformer pneumonia recognition model focused on feature enhance-ment.It incorporates three feature enhancement strategies to augment the model's feature extraction capa-bilities.The model's key components include:the Group Attention Dual Residual Module(GADRM),which leverages a dual-residual structure for effective feature fusion and enhances local feature extraction through channel shuffle,channel attention,and spatial attention;the Global-Local Feature Extraction Module(GLFEM),which applies at the network's higher levels,merging CNN and Transformer benefits to extract comprehensive global and local image features,thereby boosting the network's semantic feature extraction;and the Cross-layer Dual Attention Feature Fusion Module(CDAFFM),designed to merge shallow network spatial information with deep network channel information,enhancing the network's cross-layer feature extraction.The model's efficacy was validated through ablation and comparative experi-ments on the COVID-19 CHEST X-RAY dataset.Results demonstrate the network's high performance,with accuracy,precision,recall,F1 score,and AUC values of 98.41%,94.42%,94.20%,94.26%,and 99.65%,respectively.This model offers significant assistance to radiologists in diagnosing various pneu-monia cases using chest X-rays,marking a crucial advancement in computer-aided pneumonia diagnosis.
维普
万方数据